基于三次指数平滑法和时间卷积网络的云资源预测模型

谢晓兰; 张征征; 王建伟; 程晓春

doi:10.11959/j.issn.1000-436x.2019172

您当前的位置：

首页 >

文章列表页 >

基于三次指数平滑法和时间卷积网络的云资源预测模型

学术论文 | 更新时间：2024-06-05

- 基于三次指数平滑法和时间卷积网络的云资源预测模型
- Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network
- 通信学报 2019年40卷第8期页码：143-150
- 作者机构：
  
  1. 桂林理工大学广西嵌入式技术与智能系统重点实验室，广西桂林 541004
  2. 桂林理工大学信息科学与工程学院，广西桂林 541004
  3. 密德萨斯大学计算机科学系，伦敦 NW4 4BT
- 作者简介：
  
  [ "谢晓兰（1974- ），女，广西桂林人，博士，桂林理工大学教授、博士生导师，主要研究方向为云计算、并行计算、大数据、地球物理勘查与信息技术。" ]
  [ "张征征（1994- ），女，山东临沂人，桂林理工大学硕士生，主要研究方向为云计算、大数据。" ]
  [ "王建伟（1993- ），男，河南周口人，桂林理工大学硕士生，主要研究方向为云计算、大数据。" ]
  [ "程晓春（1973- ），男，吉林长春人，博士，密德萨斯大学计算机研究项目管理员，主要研究方向为智能计算、通信数据分析管理、通信安全。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61762031);广西创新驱动重大专项基金资助项目(2018AA32003);广西重点研发计划基金资助项目(AB17195029);广西重点研发计划基金资助项目(AB18126006);广西硕士研究生创新基金资助项目(YCSW2017156);广西硕士研究生创新基金资助项目(YCSW2018157);广西中青年教师基础能力提升基金资助项目(KY2016YB184)
- DOI：10.11959/j.issn.1000-436x.2019172
  中图分类号： TP393
- 网络首发：2019-08，
  
  纸质出版：2019-08-25
- 稿件说明：
移动端阅览
谢晓兰, 张征征, 王建伟, 等. 基于三次指数平滑法和时间卷积网络的云资源预测模型[J]. 通信学报, 2019,40(8):143-150.

Xiaolan XIE, Zhengzheng ZHANG, Jianwei WANG, et al. Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network[J]. Journal on Communications, 2019, 40(8): 143-150.
谢晓兰, 张征征, 王建伟, 等. 基于三次指数平滑法和时间卷积网络的云资源预测模型[J]. 通信学报, 2019,40(8):143-150. DOI： 10.11959/j.issn.1000-436x.2019172.

Xiaolan XIE, Zhengzheng ZHANG, Jianwei WANG, et al. Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network[J]. Journal on Communications, 2019, 40(8): 143-150. DOI： 10.11959/j.issn.1000-436x.2019172.

摘要

以Docker和Kubernetes为代表的容器云具有额外的资源开销更小、启动销毁时间更短等优点，但它仍然存在过度供应和供应不足等资源管理问题。为了使Kubernetes集群对部署在其上的应用资源使用量能“提前”响应，并根据预测值为应用及时、准确、动态地调度和分配资源，提出了一种基于三次指数平滑法和时间卷积网络的云资源预测模型，根据历史数据预测未来的资源需求。为了找到参数的最优组合，使用TPOT调参思想对参数进行优化。对Google数据集CPU和内存的预测实验表明，所提模型与其他模型相比具有更好的预测性能。

Abstract

The container cloud represented by Docker and Kubernetes has the advantages of less additional resource overhead and shorter start-up and destruction time.However there are still resource management issues such as over-supply and under-supply.In order to allow the Kubernetes cluster to respond “in advance” to the resource usage of the applications deployed on it

and then to schedule and allocate resources in a timely

accurate and dynamic manner based on the predicted value

a cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network was proposed

based on historical data to predict future demand for resources.To find the optimal combination of parameters

the parameters were optimized using TPOT thought.Experiments on the CPU and memory of the Google dataset show that the model has better prediction performance than other models.

关键词

Keywords

references

BERNSTEIN D . Containers and cloud:from LXC to docker to Kubernetes [J ] . IEEE Cloud Computing , 2014 , 1 ( 3 ): 81 - 84 .

FU S , LIU J , CHU X , et al . Toward a standard interface for cloud providers:the container as the narrow waist [J ] . IEEE Internet Computing , 2016 , 20 ( 2 ): 66 - 71 .

VARGHESE B , SUBBA L T , THAI L , et al . Container-based cloud virtual machine benchmarking [C ] // 2016 IEEE International Conference on Cloud Engineering (IC2E) . IEEE , 2016 : 192 - 201 .

XIE X L , YUAN T W , ZHOU X , et al . Research on trust model in container-based cloud service [J ] . Computers,Materials and Continua , 2018 , 56 ( 2 ): 273 - 283 .

ISLAM S , KEUNG J , LEE K , et al . Empirical prediction models for adaptive resource provisioning in the cloud [J ] . Future Generation Computer Systems , 2012 , 28 ( 1 ): 155 - 162 .

DI S , KONDO D , CIRNE W . Characterization and comparison of cloud versus grid workloads [C ] // 2012 IEEE International Conference on Cluster Computing . IEEE , 2012 : 230 - 238 .

XU D , YANG S , LIU R . A mixture of HMM,GA,and Elman network for load prediction in cloud-oriented data centers [J ] . Journal of Zhejiang University:Science C , 2013 , 14 ( 11 ): 845 - 858 .

BASHAR A , . Autonomic scaling of cloud computing resources using BN-based prediction models [C ] // 2013 IEEE 2nd International Conference on Cloud Networking . IEEE , 2013 : 200 - 204 .

HU R , JIANG J , LIU G , et al . KSwSVR:a new load forecasting method for efficient resources provisioning in cloud [C ] // 2013 IEEE International Conference on Services Computing . IEEE , 2013 : 120 - 127 .

RASHEDUZZAMAN M , ISLAM M A , RAHMAN R M . Workload prediction on Google cluster trace [J ] . International Journal of Grid and High Performance Computing , 2014 , 6 ( 3 ): 34 - 52 .

ZHONG W , ZHUANG Y , SUN J , et al . A load prediction model for cloud computing using PSO-based weighted wavelet support vector machine [J ] . Applied Intelligence , 2018 , 48 ( 11 ): 4072 - 4083 .

SHYAM G K , MANVI S S . Virtual resource prediction in cloud environment:a Bayesian approach [J ] . Journal of Network and Computer Applications , 2016 , 65 : 144 - 154 .

JIANG Y , PERNG C , LI T , et al . Asap:a self-adaptive prediction system for instant cloud resource demand provisioning [C ] // 2011 IEEE 11th International Conference on Data Mining . IEEE , 2011 : 1104 - 1109 .

ZHANG Q , ZHANI M F , BOUTABA R , et al . Harmony:dynamic heterogeneity-aware resource provisioning in the cloud [C ] // 2013 IEEE 33rd International Conference on Distributed Computing Systems . IEEE , 2013 : 510 - 519 .

CALHEIROS R N , MASOUMI E , RANJAN R , et al . Workload prediction using ARIMA model and its impact on cloud applications’ QoS [J ] . IEEE Transactions on Cloud Computing , 2015 , 3 ( 4 ): 449 - 458 .

田鹤 , 赵海 , 王进法 , 等 . 互联网传播行为的时序演化与预测 [J ] . 通信学报 , 2018 , 39 ( 6 ): 116 - 126 .

TIAN H , ZHAO H , WANG J F , et al . Timing evolution and prediction of Internet transmission behavior [J ] . Journal on Communications , 2018 , 39 ( 6 ): 116 - 126 .

冯宁 , 郭晟楠 , 宋超 , 等 . 面向交通流量预测的多组件时空图卷积网络 [J ] . 软件学报 , 2019 , 30 ( 3 ): 759 - 769 .

FENG N , GUO S N , SONG C , et al . Multi-component spatial-temporal graph convolution networks for traffic flow forecasting [J ] . Journal of Software , 2019 , 30 ( 3 ): 759 - 769 .

屈景怡 , 叶萌 , 渠星 . 基于区域残差和LSTM网络的机场延误预测模型 [J ] . 通信学报 , 2019 , 40 ( 4 ): 149 - 159 .

QU J Y , YE M , QU X . Airport delay prediction model based on regional residual and LSTM network [J ] . Journal on Communications , 2019 , 40 ( 4 ): 149 - 159 .

BAI S , KOLTER J Z , KOLTUN V . An empirical evaluation of generic convolutional and recurrent networks for sequence modeling [J ] . arXiv Preprint,arXiv:1803.01271 , 2018

BUI D M , NGUYEN H Q , YOON Y I , et al . Gaussian process for predicting CPU utilization and its application to energy efficiency [J ] . Applied Intelligence , 2015 , 43 ( 4 ): 874 - 891 .

BARATI M , SHARIFIAN S . A hybrid heuristic-based tuned support vector regression model for cloud load prediction [J ] . The Journal of Supercomputing , 2015 , 71 ( 11 ): 4235 - 4259 .

HU W , YAN L , LIU K , et al . A short-term traffic flow forecasting method based on the hybrid PSO-SVR [J ] . Neural Processing Letters , 2016 , 43 ( 1 ): 155 - 172 .

CHEN N , AGARWAL A , WIERMAN A , et al . Online convex optimization using predictions [C ] // ACM SIGMETRICS Performance Evaluation Review . ACM , 2015 : 191 - 204 .

HUANG L , CHEN M , LIU Y . Learning-aided stochastic network optimization with imperfect state prediction [C ] // The 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing . ACM , 2017 :12.

CHEN K , HUANG L . Timely-throughput optimal scheduling with prediction [J ] . IEEE/ACM Transactions on Networking , 2018 , 26 ( 6 ): 2457 - 2470 .

CAO J , FU J , LI M , et al . CPU load prediction for cloud environment based on a dynamic ensemble model [J ] . Software:Practice and Experience , 2014 , 44 ( 7 ): 793 - 804 .

SUBIRATS J , GUITART J . Assessing and forecasting energy efficiency on cloud computing platforms [J ] . Future Generation Computer Systems , 2015 , 45 : 70 - 94 .

MESSIAS V R , ESTRELLA J C , EHLERS R , et al . Combining time series prediction models using genetic algorithm to autoscaling web applications hosted in the cloud infrastructure [J ] . Neural Computing and Applications , 2016 , 27 ( 8 ): 2383 - 2406 .

KAUR G , BALA A , CHANA I . An intelligent regressive ensemble approach for predicting resource usage in cloud computing [J ] . Journal of Parallel and Distributed Computing , 2019 , 123 : 1 - 12 .

HUANG J , LI C , YU J . Resource prediction based on double exponential smoothing in cloud computing [C ] // 2012 2nd International Conference on Consumer Electronics,Communications and Networks . IEEE , 2012 : 2056 - 2060 .

YOU J , WANG Y , PAL A , et al . Hierarchical temporal convolutional networks for dynamic recommender systems [C ] // The World Wide Web Conference . ACM , 2019 : 2236 - 2246 .

HE K , ZHANG X , REN S , et al . Deep residual learning for image recognition [C ] // IEEE Conference on Computer Vision and Pattern Recognition . IEEE , 2016 : 770 - 778 .

OLSON R S , URBANOWICZ R J , ANDREWS P C , et al . Automating biomedical data science through tree-based pipeline optimization [C ] // European Conference on the Applications of Evolutionary Computation . Springer , 2016 : 123 - 137 .

浏览量

1537

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

云边端场景下基于多智能体深度强化学习的边缘缓存策略

异构无线网络中多模终端多接入选择机制研究

绿色发光粉CaBa₂(BO₃)₂ ∶ Tb³⁺的制备和发光特性

In_0.2Ga_0.8As/GaAs单量子阱PL谱温度特性及其机制

激发波长和Eu²⁺的掺杂量对Sr₄Al₁₄O₂₅ ∶ Eu²⁺ , Dy³⁺发光性能的影响